The Application of BIM-Based OHSMS Information Systems to Improve Safety Performance
Bibliographic record
Abstract
The accidents recorded during university construction projects usually affect the safety performance of the institution. This indicates it is important to recognize the hazards and construction risks at the design stage in order to have an optimal safety plan. Moreover, the university area has several organizations involved in the implementation of Occupational Health and Safety Management Systems (OHSMS). Therefore, there is a need to develop the OHSMS information system to accommodate the role of the parties involved and to disseminate information and communication more effectively. This study was conducted to develop a WBS and BIM-based safety plan for the OHSMS information systems to improve the safety performance using the case study method. The result showed that the WBS is the basic constituent of the safety plan integrated with the BIM to visualize the project in real terms in order to ensure easier identification of hazards and risks. The process involved building data security information in BIM to develop web-based information systems and the BIM-based OHSMS information system developed is expected to improve safety performance in the university area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".